Explicit discrimination and ingroup favoritism, but no implicit biases in hypothetical triage decisions during COVID-19

Disturbingly realistic triage scenarios during the COVID-19 pandemic provide an opportunity for studying discrimination in moral reasoning. Biases and favoritism do not need to be explicit and overt, but can remain implicit and covert. In addition to assessing laypeople’s propensity for engaging in overt discrimination, the present study examines whether they reveal implicit biases through seemingly fair random allocations. We present a cross-sectional online study comprising 6 timepoints and a total of 2296 participants. Each individual evaluated 19 hypothetical scenarios that provide an allocation dilemma between two patients who are in need of ventilation and differ only in one focal feature. Participants could either allocate the last ventilator to a patient, or opt for random allocation to express impartiality. Overall, participants exhibited clear biases for the patient who was expected to be favored based on health factors, previous ethical or caretaking behaviors, and in-group favoritism. If one patient had been pre-allocated care, a higher probability of keeping the ventilator for the favored patient indicates persistent favoritism. Surprisingly, the absence of an asymmetry in random allocations indicates the absence of covert discrimination. Our results demonstrate that laypeople’s hypothetical triage decisions discriminate overtly and show explicit biases.

1 Supplementary methods

Example scenarios
Supplementary Figure 1.Example scenarios as seen by the participants in the baseline condition without a default (upper panel) and with a default (lower panel).

Supplementary Measures
To address in-group favoritism we collected participant characteristics that were related to our patient features.Political views and religiosity were assessed with simple items using a 7-point scale (from conservative to liberal) and non-religious to religious as used by 1 .We asked about alcohol consumption as standard drinks per day 2 .In our analysis, we contrasted moderate alcohol consumption (0 to 2 standard drinks per day) with hazardous alcohol consumption (more than 2 standard drinks per day).To assess participants' weight, we asked them to provide their height and weight (to calculate their BMI) and excluded participants with implausible entries.Finally, we asked participants whether they are registered as an organ donor, whether they got the seasonal flu shot, and assessed their attitude towards flu vaccination on a 5-point scale (1: strongly oppose to 5: strongly favor).
Additional questions regarding time-and risk-preferences, as well as altruistic giving were simple 1-item measures taken from the preference survey module 3,4 .To assess altruism, participants indicated how much of US-$1,000 they are willing to spend on a good cause.Finally, we also assessed participants' perceptions of the COVID pandemic.We used 7-point scales translated into English from Betsch et al. 5 asking for the probability (1: extremely unlikely to 7: extremely likely) and severity of an infection (1: extremely harmless to 7: extremely severe), as well as the perceived severity of the COVID-19 outbreak in their area of residence (1: completely harmless to 7: very severe).

Detailed sample description
Supplementary Table 1 describes the collected participant characteristics for the sample used in our analyses (for the sample without exclusions see Supplementary Table 2. At the latter 2 timepoints of data collection (5 and 6) the sample tended to report higher education, higher belief in god, having more children, higher income, more likely were African American, being more likely married, being less likely born in the US, were more likely under-or normal-weight, more likely reported excellent health, but also COVID-infections.Additionally, they more likely selected the riskier of two options and donated more in a hypothetical donation task.
A change in sample composition is consistent with the observation that the participant pool on Amazon Mechanical Turk has changed during the pandemic 6 .To account for this possible change we enter timepoint of data collection into our models and show our main descriptive results also by different ranges for our timepoints of data collection.

Statistical models
Here, we describe the models used in our analyses.They include the logistic mixed models to analyze differences between features, differences between conditions of withholding and withdrawal across features, and feature-specific effects beyond the other features.We relied on mixed models whenever we had to account for non-independence of observations.All analyses were conducted in R-4.1.2 7.Generalized mixed effects models were estimated using the function glmer in lme4 8 .We omitted random effects that could not be estimated based on the available data.
We specified theoretically plausible models and tested the relevance of the corresponding predictors and their interactions.To test main effects and interactions we used the R-package car 9 .For fixed effects in logistic models (including logistic mixed models) we provide Wald-χ 2 statistics and their corresponding p-values 10 .These tests allow to test the relevance of variables in logistic models 11 , including predictors with more than 2 levels 12 , similar to omnibus-tests in an ANOVA framework.For comparisons between features we report odds ratios and p-values from post hoc tests obtained through 13 .

Feature specific models
To assess feature-specific effects we report model pairs for combinations of patient feature and decision maker characteristic, on both bias and impartiality.Effects are only feature-specific if an interaction of the patient feature with the participant characteristic explains that data better than the main effects of patient feature and participant characteristic.Otherwise, they rather reflect the general effects of the patient feature or participants general response tendencies, respectively.Therefore, models included an indicator for the focal patient feature, the decision maker characteristic, and their interaction.Since timepoints of data collection 1 to 4 differ from timepoints 5 and 6, we include an interaction for these 2 sets of timepoints and discuss robustness of our results.To address non-independence of features and individuals we include random intercepts for both.For models of bias we also include the proportion of random allocations for the respective participant and feature.
For both bias and impartiality we report Wald χ 2 statistics to qualify whether the 2-way interactions of patient feature and participant characteristic are relevant for the model.For discrete participant characteristics we report odds ratios across the remaining patient features, odds ratios for the focal patient feature and whether their differences differ from each other (OR di f f ).For continuous characteristics, we report the difference in effects between the focal and the remaining features.
Additionally, we test 3-way interactions of patient feature, participant characteristic and set of timepoints to assess if the effects differ between timepoint 1 to 4 and timepoint 5 and 6.If the interaction is relevant for the model, we report the subsequent comparisons by set of timepoints.
Supplementary Tables 6 to 11 show the models tested.

Full test statistics for bias and impartiality on patient features
See next page.

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Supplementary Table 3. Probability of random allocation and favored among non-random allocations (with p-values for difference to 0.5) for each patient pair.The total number of allocations was N=1362.

Robustness to timepoint of data collection
Supplementary Figure 2 shows that allocation patterns across features differed between the first 4 and the last 2 timepoints of data collection.Therefore, we investigate the impact of these two time windows on our results.Overall we find that the general pattern of results is robust to timepoint of data collection (see Supplementary Figure 3).Despite decreased random allocations, the direction of bias remains the same across timepoints.Still, overall it appears that bias is decreased for several features through more allocations to the unfavored patient.Next we discuss the robustness of the results reported in the main text.
Health and longevity For all health and longevity features bias is decreased in the last 2 timepoints of data collection.Most remarkably biases based on the diabetes feature and the wheelchair feature largely disappear.Except for the comparison between the hereditary diabetes feature and the wheelchair feature, the pattern of all comparisons reported in the main text remains unchanged (although some effects fail to reach conventional levels of statistical significance).
Comparing patients differing in the presence of hereditary diabetes and patients differing whether they sit in a wheelchair, absent evidence for bias is robust from the first 4 timepoints (OR = 1.02, p = 0.903) to the last 2 timepoints (OR = 0.86, p = 0.440).Similarly, the direction of effects is the same as across timepoints with lower impartiality for the diabetes features (OR = 0.31, p < 0.001) but no discernible difference in the last 2 timepoints (OR = 0.95, p = 0.735).Thus, perceptions of

Disfavored allocations
Supplementary Figure 2. Allocations at each timepoint of data collection next to COVID-19 cases, deaths, and 7-day incidence (and events) around these timepoints.We observe no clear preference for random allocations, but systematic partiality in favor of the patient we expected to be favored.Moreover, our data show a systematic decrease in random allocations indicating higher relevance of features and a decrease in partiality, indicating less strong favoritism for the overall favored patient.
different kinds of illness or frailty may differ based on the surveyed participants or temporal changes in attitudes.The change in effects appears mainly driven by changes in the wheelchair feature (Supplementary Figure 3).
Similarly, comparing patient age and diabetes, the effect sizes differ, whereas the patterns for bias (timepoints 1 to 4: OR = 1.66, p = 0.001 and timepoints 5 and 6: OR = 2.46, p < 0.001) and impartiality (timepoints 1 to 4: OR = 0.50, p < 0.001 and timepoints 5 and 6: OR = 0.67, p = 0.020 respectively) are robust.Finally, contrasting patient age and alcohol consumption, decreased bias is largely limited to the first 4 timepoints (OR = 0.54, p < 0.001, despite an effect in the same direction in timepoints 5 and 6: OR = 0.78, p = 0.210).Correspondingly, the decreased impartiality on age relative to alcohol consumption is clearly limited to timepoints 1 to 4 (OR = 0.57, p < 0.001, timepoints 5 and 6: OR = 0.93, p = 0.660).Thus, the age and alcohol consumption features were treated more similarly to each other over time.Overall, these robustness checks underscore that controllable features play a substantive role for participants' allocation decisions, whereas considerations of frailty may vary with situational factors or sample characteristics.
Ethical behaviors When patients features differed in their charitable behavior and flu vaccination status, systematic choices of random allocations reflecting impartiality are limited to timepoints 1 to 4 (68.6% and 63.5%, respectively) but not in the last 2 timepoints (charitable behavior: 47.3%) with a reversed preference for non-random allocations for vaccination behavior 41.2%).Similarly, organ donor status elicited fewer random responses than than charitable behavior and vaccination in the first 4 timepoints (55.6%) and even fewer in the latter 2 (39.6%).The latter two findings could imply an increase in polarization on the topics of vaccination and organ donation or an increase in selecting response options randomly, resulting in a more even distribution of the 3 response categories.The clear decline in impartiality relative to the other features when one patient was described to evade taxes persisted from the first 4 timepoints (45.9%) to the last 2 timepoints (34.0%), attesting to the robustness of pervasive punishment of non-cooperative or even delinquent behavior.
Family ties and caretaking Favoritism for those with family ties persists but is weakened in later timepoints of data collection.Participants were less impartial towards married patients than towards those with adult children (past caretaking) in timepoints 1 Supplementary Figure 3. Allocation proportions by patient feature across timepoints of data collection.The left label corresponds to the patient who we expected to be disfavored and the right label to the patient who we expected to be favored.Asterisks indicate that random allocations significantly deviated from 50%, and that were biased towards the favored patient, respectively (binomial tests against 50%, first uncorrected, second Bonferroni corrected).The offset of bars indicates bias the as the absolute difference between favored and disfavored allocations in percent and odds ratios right of the bars provide effect sizes of bias   was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U. 13/21

Model table for random allocations and the probaiblity that the default keeps the ventilator
Supplementary Table 5.Estimates from a logistic mixed model predicting the probability of (1) random allocations and (2) whether a patient keeps the ventilator when one of the patients arrived earlier (default).If there was no default (reference condition) keeping was defined as the favored patient receiving the ventilator.Results are controlled for whether the timepoint of data collection was among the first 4 or not.

Robustness of overt instead of covert discrimination to timepoint of data collection
Comparisons of the default conditions to the witholding condition in the first 4 and last 2 timepoints reveal that our results are robust to differences in participant behavior.For both earlier and later timepoints, the probability that the favored default could keep the ventilator was consistently larger compared to allocating it to the favored patient in situations of withholding (OR = 5.01, p < 0.001 and OR = 3.64, p < 0.001).The probability that the unfavored default kept the ventilator was larger than allocating it to the favored patient in the withholding condition in timepoints 1 to 4 (OR = 1.71, p < 0.001) without evidence for the effect in timepoints 5 and 6 (OR = 1.25, p = 0.301).For both timepoints random allocations were less likely for both the favored (OR = 0.13, p < 0.001 and OR = 0.35, p < 0.001) and unfavored patient (OR = 0.11, p < 0.001 and OR = 0.30, p < 0.001).Differences at the last 2 timepoints were weaker, because the bias towards the favored individual was smaller and random allocations were generally more rare.Note that comparisons between favored and unfavored default patients were not affected by the last 2 timepoints of data collection (5 and 6) because the experimental manipulation was limited to timepoints 3 and 4, so that we do not know if responses to situations of withdrawing care would have been different in later timepoints.

Feature-specific differences in overt discrimination effects by participant characteristics indicate in-group favoritism
Beyond main effects of patient features we find evidence for interactions of patient features with participant characteristics, reflecting in-group favoritism and decision maker ideology (see Supplementary Figure 5).We distinguish feature specific differences in partiality and bias between participants from response tendencies that are general to patient features or participant characteristics by means of logistic mixed models.By testing the interaction between the feature and the decision maker interaction, we show the effect that a combination of a patient feature and its corresponding participant characteristic have beyond the underlying tendencies (see Supplementary Methods; for model tables see Supplementary Tables 6 to 12).For absent differences in bias, differences in impartiality still indicate in-group favoritism, because a larger proportion of favored individuals receives the ventilator.
Age Supplementary Table 6 shows the feature specific model for patient and participant age.There is no evidence for an interaction between participant age and the age feature for bias [χ 2 (1) = 0.028, p = 0.868] or impartiality [χ 2 (1) = 0.493, p = 0.483].Absent evidence for moderating influence of timepoint of data collection on bias [χ 2 (1) = 0.003, p = 0.956] or impartiality [χ 2 (1) = 1.983, p = 0.159] indicates robustness of this finding.Thus, seemingly lower bias in favor of the younger patient among younger participants in Supplementary Figure 5 does not appear to be specific to patient age and reflects the absence of additional ageism.
Organ donation For organ donation we find evidence for more willingness to discriminate among participants who were registered as organ donors themselves (Supplementary Table 6).Although we do not find evidence for a feature specific effect on bias [χ 2 (1) = 2.247, p = 0.134, robust to later timepoints χ 2 (1) = 0.145, p = 0.703], there is evidence for feature-specific differences in impartiality as a function of whether participants were registered as organ donors or not [χ 2 (1) = 26.543,p < was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U.S. was born and raised in the U.S. recently arrived in the U.S. with a work permit recently arrived in the U.S. with a work permit recently arrived in the U.S. with a work permit recently arrived in the U.S. with a work permit recently arrived in the U.S. with a work permit recently arrived in the U.S. with a work permit  Supplementary Figure 5. Feature specific allocation patterns by participant groups corresponding to patient features.We observe in-group favoritism for a large number of patient features.The left label corresponds to the patient who we expected to be disfavored and the right label to the patient who we expected to be favored.Asterisks indicate that random allocations significantly deviated from 50%, and that favored allocations were significantly more likely that disfavored allocations, respectively (binomial tests against 50%, first uncorrected, second Bonferroni corrected).The offset of bars indicates bias the as the absolute difference between favored and disfavored allocations in percent and odds ratios right of the bars provide effect sizes of partiality.
Supplementary Table 6.Estimates from a logistic mixed model predicting allocations to the young patient as a function of participants' age.Model (1) predicts allocations to the the young patient among non-random allocations (bias) and Model (2) random allocations.

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Supplementary Table 7. Estimates from a logistic mixed model predicting allocations to the organ donor as a function of whether participants reported being registered as organ donors.Model (1) predicts allocations to the the organ donor among non-random allocations (bias) and Model (2) random allocations.

Dependent variable:
Bias: + p < 0.1; * p < 0.05; * * p < 0.01; * * * p < 0.001.0.001] with an interaction with later timepoints [χ 2 (1) = 4.353, p = 0.037)].Organ donors tended to be more impartial across other features (OR = 1.27, p = 0.044, but not at the later timepoints OR = 0.77, p = 0.142), whereas they were consistently less impartial when patients differed in their organ donation status (OR = 0.44, p < 0.001 and OR = 0.49, p = 0.014).The difference in change from other features to organ donation was substantive in the first 4 timepoints (OR di f f erence = 0.34, p < 0.001) and pointed into the same direction at the later timepoints (OR di f f erence = 0.64, p = 0.064), despite overall lower impartiality.Thus, it appears that participants who were willing to donate their organs favored patients who were described to be similar to them in this regard.In line with a role of attitude, impartiality decreased with a more positive attitude towards flu vaccination (β di f f = −0.18,p = 0.029).The results for self-reported vaccination behavior on impartiality were less clear: Whereas participants who reported being vaccinated against the flu tended were less impartial across vaccination-unrelated patient features both in the first 4 (OR = 0.71, p = 0.016) and marginally in the last 2 timepoints (OR = 0.71, p = 0.061), they were even less impartial when patients differed in whether they were vaccinated against the flu in the first 4 timepoints (OR = 0.45, p = 0.001) but clearly not in the last 2 timepoints (OR = 1.18, p = 0.575).This corresponds to a stronger decrease in impartiality relative to other features in the first 4 timepoints (OR di f f = 0.63, p = 0.021) but even an increase in impartiality in the latter 2 (OR di f f = 1.67, p = 0.042).Thus, whereas we find clear evidence for ingroup favoritism among the vaccinated in the first 4 timepoints, we do not find this in the latter timepoints where vaccinated individuals appear to be even more impartial towards patients who are unwilling to get vaccinated against the flu compared to other features.Overall, this is evidence that an individual's attitude towards flu vaccination is a strong predictor of whether a vaccinated patient is more likely to be saved, replicating and extending previous work by 14 .Conversely, it is less clear whether vaccination behavior constitutes a marker of group membership that is relevant for discrimination beyond attitude.

Patient sex
In line with well-established biases, we also find in-group favoritism based on demographics and group membership.We find indication of sexism.Bias towards the female patient specifically varied with participant sex [χ 2 (1) = 11.383,p = 0.001], whereas there was no evidence for differences in impartiality impartiality did not [χ 2 (1) = 0.021, p = 0.884].Although females were also more biased than males towards the favored patient across other features [OR = 1.34, p = 0.002], this difference in bias was even more pronounced when patients differed by sex [OR = 3.12, p < 0.001, OR di f f erence = 2.33, p = 0.001].Absent evidence for moderating influence of timepoint of data collection on bias [χ 2 (1) = 0.727, p = 0.394] or impartiality [χ 2 (1) = 0.174, p = 0.677] indicates robustness of these findings.This indicates that overall favoritism for female patients is increased among females and largely removed among males.
Ethnicity When deciding based on patient ethnicity, we find evidence for feature-specific differences in bias [χ 2 (2) = 13.286,p = 0.001] and impartiality [χ 2 (2) = 11.749,p = 0.003] as a function of participant ethnicity.Participants reporting to be African American were less biased towards the favored patient across features than white participants (OR = 0.47, p < 0.001) or other ethnicities (OR = 0.50, p < 0.001), with no difference between white participants and other ethnicities (OR = 0.94, p = 0.973).However, when patients differed in their ethnicity, the decrease in bias among African Americans was not more pronounced compared to whites (OR = 0.73, p = 0.903) but compared to other ethnicities (OR = 0.23, p < 0.001), also with

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Supplementary Table 10.Estimates from a logistic mixed model predicting allocations to the female patient as a function of participants' gender.Model (1) predicts allocations to the the female patient among non-random allocations (bias) and Model (2) random allocations.higher bias among whites than among other ethnicities (OR = 3.14, p = 0.045).The change relative to non-ethnicity features differed between African American participants and whites (OR = 0.47, p = 0.015), whites and other ethnicities (OR = 0.30, p = 0.026), but not between African Americans and other ethnicities (OR = 0.64, p = 0.745).Although Figure 2 shows reversed bias towards the white patient among non-whites, the bias is estimated to be decreased but not reversed after controlling for general response tendencies [bias(white) = 0.56, p = 0.622].Moreover, participants reporting to be African American were less impartial towards the favored patient across features than white participants (OR = 0.58, p = 0.030) or other ethnicities (OR = 0.56, p < 0.001), with no difference between white participants and participants from other ethnicities (OR = 1.03, p = 0.999).However, when patients differed in their ethnicity, the decrease in impartiality among African Americans tended to be more pronounced compared to whites (OR = 0.30, p = 0.003) and other ethnicities (OR = 0.36, p < 0.001).An effect suggesting lower impartiality among whites than among other ethnicities cannot be distinguished from chance in the current data (OR = 0.82, p = 0.906), likely because of few observations of the "other category".The change in the effect of participant ethnicity from non-ethnicity fatures to other features differed marginally between African American participants and Whites (OR = 0.64, p = 0.061) and between African Americans and other ethnicities (OR = 1.94, p = 0.097) but not between Whites and other ethnicities (OR = 1.25, p = 0.812).
Absent evidence for moderating influence of timepoint of data collection on bias [χ 2 (2) = 1.220, p = 0.543] or impartiality [χ 2 (2) = 2.762, p = 0.251] indicates robustness of these findings.Thus, white participants were more biased than participants reporting to be African American, both overall but specifically when patients differed in their ethnicity.Thereby, African American participants tended to be less impartial, with suggestive evidence that this was stronger when patients differed in their ethnicity.
Political orientation (and foreigners) Discrimination of patients with migration background varies as a function of participants' political orientation.Beyond immediate group membership, political orientation affected how participants decided between U.S.-born patients and migrants.When patients with a migration background had to be traded off with a a U.S.-born

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Supplementary Table 11.Estimates from a logistic mixed model predicting allocations to the patient who believed in god as a function of participants' religiosity.Model (1) predicts allocations to the the patient who believed in god among non-random allocations (bias) and Model (2) random allocations.Although compared to the remaining features we find a more negative trend on impartiality for both the refugee (β di f f = −0.61,p < 0.001) and the work permit holder (β di f f = −0.70,p < 0.001) throughout the first 4 timepoints of data collection, in the last 2 timepoints the effect for the refugee disappears (β di f f = 0.01, p = 0.900) and the effect for the work permit holder diminshes so far that we at least cannot detect it anymore (β di f f = −0.14, p = 0.249).
This extends the finding by 15 that patient nationality was more relevant among more conservative decision makers.Additionally, our results suggest that political orientation and own migration status may act on different facets of the decision, 19/21 namely, how many show a bias and how strong the bias is.However, the result is only robust to the first 4 timepoints of data collection, which could indicate a change in the perceptions, less careful responding and should be scruitinzed by future work.
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Supplementary Figure 4 .
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Table 1 .
Description of participant characteristics for the sample with exclusions at each timepoint of data collection.

Table 2 .
Description of survey information for the sample without exclusions at each timepoint of data collection.

8/21 2.1.2 Description of patient features SupplementaryTable 4 .
Estimates from a logistic mixed model predicting the probability of (1) favored allocations among non-random allocations (bias) and (2) random allocations (impartiality) as a function of patient feature, timepoint, controlling for the number of non-random allocations on the participant and feature level in model 1.

Table 8 .
Estimates from a logistic mixed model predicting allocations to the patient who was vaccinated against the flu as a function of participants' attitude towards flu vaccination and whether participants reported being vaccinated against the flu.Model (1) predicts allocations to the the patient who was vaccinated against the flu among non-random allocations (bias) and Model (2) random allocations.

Table 9 .
2accination Next, we considered the effects of patients' and participants attitudes and behaviors towards flu vaccination.A model including feature-specific interactions for flu vaccination attitude and self-reported vaccination behavior reveal feature specific effects on bias for participants attitude towards flu vaccination [χ2(1) = 7.667, p = 0.006, robust to timepoint of Estimates from a logistic mixed model predicting allocations to the female patient as a function of participants' gender.Model(1)predicts allocations to the the female patient among non-random allocations (bias) and Model (2) random allocations.